The CoMLSim is compared with other ML baselines namely, UNet [1]}, FNO [2]} and DeepONet [3]} for the experiments outlined in Section . We compare the mean absolute error with respect to Ansys Fluent and averaged over the unseen testing cases and all solution variables. In the case of 3-D chip cooling we report the \(L_{\infty }\)  norm of temperature as this metric is more suited to this industrial application. It may be observed from Table REF  that the CoMLSim approach performs better than other ML baselines. All the ML methods perform better for the non-linear Poisson's case as compared to the linear Poisson's because the source term is less stiff but the CoMLSim approach does a better job in modeling the non-linearity arising due solution coupling. It must be noted that CoMLSim as well as the baselines are trained with reasonably training samples. The baseline performance can have different outcomes with increasing the size of the training data.
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